Assigning a Grade: Accurate Measurement of Road Quality Using Satellite Imagery

TL;DR

This paper presents a satellite imagery-based model for assessing road quality, achieving high accuracy in classification tasks, which could revolutionize infrastructure monitoring especially in developing regions.

Contribution

The study introduces a novel approach combining high-resolution satellite imagery with CNNs for accurate, scalable road quality assessment, addressing limitations of traditional methods.

Findings

Binary classification accuracy of 88%

Five-category classification accuracy of 73%

Robustness demonstrated in challenging scenarios

Abstract

Roads are critically important infrastructure to societal and economic development, with huge investments made by governments every year. However, methods for monitoring those investments tend to be time-consuming, laborious, and expensive, placing them out of reach for many developing regions. In this work, we develop a model for monitoring the quality of road infrastructure using satellite imagery. For this task, we harness two trends: the increasing availability of high-resolution, often-updated satellite imagery, and the enormous improvement in speed and accuracy of convolutional neural network-based methods for performing computer vision tasks. We employ a unique dataset of road quality information on 7000km of roads in Kenya combined with 50cm resolution satellite imagery. We create models for a binary classification task as well as a comprehensive 5-category classification task, with accuracy scores of 88 and 73 percent respectively. We also provide evidence of the robustness of our methods with challenging held-out scenarios, though we note some improvement is still required for confident analysis of a never before seen road. We believe these results are well-positioned to have substantial impact on a broad set of transport applications.